Inferring causality and functional significance of human coding DNA variants

Sunyaev, Shamil

doi:10.1093/hmg/dds385

Cited by 46 publications

(45 citation statements)

References 77 publications

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“…The c.290dupA mutation identified in this Azorean patient is recurrent, as it was also recently described in a Japanese patient with multiple BCCs, jaw OKCs, calcification of the falx cerebri, and a meningioma at the age of 66 years (Kijima et al, 2012). These 2 patients had a different clinical outcome, indicating the involvement of modifier genes that are distinct from the disease locus, as identified for instance in neurofibromatosis type 1 (Bahuau et al, 2001;Ponti et al, 2011), or may have occurred because of different genetic backgrounds (Sunyaev, 2012). In addition, we cannot exclude the effects of epigenetic and environmental factors on the final NBCCS phenotypic outcome, as the same PTCH1 mutation in monozygotic twins displays different clinical features (Matsuzawa et al, 2006).…”

Section: Discussionsupporting

confidence: 70%

Case Report Multiple nevoid basal cell carcinoma syndrome associated with congenital orbital teratoma, caused by a PTCH1 frameshift mutation

Rodrigues¹,

Carvalho²,

Cabral³

et al. 2014

Genet. Mol. Res.

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Section: Discussionsupporting

confidence: 70%

Case Report Multiple nevoid basal cell carcinoma syndrome associated with congenital orbital teratoma, caused by a PTCH1 frameshift mutation

Rodrigues¹,

Carvalho²,

Cabral³

et al. 2014

Genet. Mol. Res.

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“…It may be possible to glean information about whether missense alleles are null or neutral-either from biochemical experiments (24) (where in vitro assays are available) or computational programs, such as PolyPhen-2, SIFT, or MutationTaster (reviewed in ref. 46)-that offer computational predictions of whether a mutation is likely to be damaging. Given the quality of the predictions (proportions, γ null and γ neutral , of true null and neutral variants declared to be damaging), one can calculate the optimal threshold T* and the corresponding sample size.…”

Section: Impact Of Population Expansion On the Genetic Architecture Ofmentioning

confidence: 99%

Searching for missing heritability: Designing rare variant association studies

Zuk

Schaffner

Samocha

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

582

559

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Genetic studies have revealed thousands of loci predisposing to hundreds of human diseases and traits, revealing important biological pathways and defining novel therapeutic hypotheses. However, the genes discovered to date typically explain less than half of the apparent heritability. Because efforts have largely focused on common genetic variants, one hypothesis is that much of the missing heritability is due to rare genetic variants. Studies of common variants are typically referred to as genomewide association studies, whereas studies of rare variants are often simply called sequencing studies. Because they are actually closely related, we use the terms common variant association study (CVAS) and rare variant association study (RVAS). In this paper, we outline the similarities and differences between RVAS and CVAS and describe a conceptual framework for the design of RVAS. We apply the framework to address key questions about the sample sizes needed to detect association, the relative merits of testing disruptive alleles vs. missense alleles, frequency thresholds for filtering alleles, the value of predictors of the functional impact of missense alleles, the potential utility of isolated populations, the value of gene-set analysis, and the utility of de novo mutations. The optimal design depends critically on the selection coefficient against deleterious alleles and thus varies across genes. The analysis shows that common variant and rare variant studies require similarly large sample collections. In particular, a well-powered RVAS should involve discovery sets with at least 25,000 cases, together with a substantial replication set. mapping disease genes | power analysis | statistical genetics G enomic studies over the last half decade have shed light on the genetic basis of common polygenic human diseases and traits, identifying thousands of loci and revealing key biological pathways. Nonetheless, the genetic variants identified thus far appear to explain less than half of the estimated heritability in most diseases and traits. The sources of the so-called missing heritability remain unclear (1). This article is our second paper exploring the mystery of missing heritability.In our first paper (2), we explored a methodological issue. We showed that genetic interactions, if present, could account for substantial missing heritability. Still, this is likely to be only a partial explanation. In this paper, we turn to the search for additional genetic variants underlying common human diseases, focusing on rare genetic variants.The discovery of genes underlying common diseases depends on association studies (except in special cases where Mendelian subtypes of common diseases show clear segregation in large families). Association studies involve testing whether the frequency of a set of one or more alleles differs between cases and a control population, indicating that the set of alleles is associated with the disease. Association studies to date have largely focused on studying individual common variants, because ...

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“…One of the main NGS challenges is an overwhelming volume of data generated [252]. It is also challenging to store, analyse [256] and translate this amount of genetic and genomic data into medical and biological context [3,256,257].…”

Section: Discussionmentioning

confidence: 99%